Overview

Dataset statistics

Number of variables24
Number of observations195
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.7 KiB
Average record size in memory192.7 B

Variable types

Categorical2
Numeric22

Alerts

name has a high cardinality: 195 distinct values High cardinality
MDVP:Fo(Hz) is highly correlated with MDVP:Fhi(Hz) and 1 other fieldsHigh correlation
MDVP:Fhi(Hz) is highly correlated with MDVP:Fo(Hz)High correlation
MDVP:Jitter(%) is highly correlated with MDVP:Jitter(Abs) and 13 other fieldsHigh correlation
MDVP:Jitter(Abs) is highly correlated with MDVP:Fo(Hz) and 15 other fieldsHigh correlation
MDVP:RAP is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:PPQ is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
Jitter:DDP is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:Shimmer is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
MDVP:Shimmer(dB) is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
Shimmer:APQ3 is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
Shimmer:APQ5 is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
MDVP:APQ is highly correlated with MDVP:Jitter(%) and 15 other fieldsHigh correlation
Shimmer:DDA is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
NHR is highly correlated with MDVP:Jitter(%) and 15 other fieldsHigh correlation
HNR is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
status is highly correlated with spread1 and 1 other fieldsHigh correlation
RPDE is highly correlated with MDVP:Jitter(Abs) and 10 other fieldsHigh correlation
spread1 is highly correlated with MDVP:Jitter(%) and 16 other fieldsHigh correlation
spread2 is highly correlated with MDVP:APQ and 2 other fieldsHigh correlation
D2 is highly correlated with NHRHigh correlation
PPE is highly correlated with MDVP:Jitter(%) and 16 other fieldsHigh correlation
MDVP:Fo(Hz) is highly correlated with MDVP:Flo(Hz)High correlation
MDVP:Flo(Hz) is highly correlated with MDVP:Fo(Hz)High correlation
MDVP:Jitter(%) is highly correlated with MDVP:Jitter(Abs) and 13 other fieldsHigh correlation
MDVP:Jitter(Abs) is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:RAP is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:PPQ is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
Jitter:DDP is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:Shimmer is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
MDVP:Shimmer(dB) is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
Shimmer:APQ3 is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
Shimmer:APQ5 is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
MDVP:APQ is highly correlated with MDVP:Jitter(%) and 15 other fieldsHigh correlation
Shimmer:DDA is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
NHR is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
HNR is highly correlated with MDVP:Jitter(%) and 15 other fieldsHigh correlation
status is highly correlated with spread1 and 1 other fieldsHigh correlation
RPDE is highly correlated with HNR and 2 other fieldsHigh correlation
spread1 is highly correlated with MDVP:Jitter(%) and 16 other fieldsHigh correlation
spread2 is highly correlated with MDVP:APQ and 3 other fieldsHigh correlation
D2 is highly correlated with MDVP:Shimmer and 5 other fieldsHigh correlation
PPE is highly correlated with MDVP:Jitter(%) and 16 other fieldsHigh correlation
MDVP:Fo(Hz) is highly correlated with MDVP:Fhi(Hz)High correlation
MDVP:Fhi(Hz) is highly correlated with MDVP:Fo(Hz)High correlation
MDVP:Jitter(%) is highly correlated with MDVP:Jitter(Abs) and 13 other fieldsHigh correlation
MDVP:Jitter(Abs) is highly correlated with MDVP:Jitter(%) and 9 other fieldsHigh correlation
MDVP:RAP is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:PPQ is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
Jitter:DDP is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:Shimmer is highly correlated with MDVP:Jitter(%) and 11 other fieldsHigh correlation
MDVP:Shimmer(dB) is highly correlated with MDVP:Jitter(%) and 11 other fieldsHigh correlation
Shimmer:APQ3 is highly correlated with MDVP:Jitter(%) and 10 other fieldsHigh correlation
Shimmer:APQ5 is highly correlated with MDVP:Jitter(%) and 10 other fieldsHigh correlation
MDVP:APQ is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
Shimmer:DDA is highly correlated with MDVP:Jitter(%) and 10 other fieldsHigh correlation
NHR is highly correlated with MDVP:Jitter(%) and 11 other fieldsHigh correlation
HNR is highly correlated with MDVP:Jitter(%) and 10 other fieldsHigh correlation
spread1 is highly correlated with MDVP:Jitter(%) and 6 other fieldsHigh correlation
PPE is highly correlated with MDVP:Jitter(%) and 6 other fieldsHigh correlation
MDVP:Fo(Hz) is highly correlated with MDVP:Fhi(Hz) and 8 other fieldsHigh correlation
MDVP:Fhi(Hz) is highly correlated with MDVP:Fo(Hz) and 2 other fieldsHigh correlation
MDVP:Flo(Hz) is highly correlated with MDVP:Fo(Hz) and 7 other fieldsHigh correlation
MDVP:Jitter(%) is highly correlated with MDVP:Jitter(Abs) and 13 other fieldsHigh correlation
MDVP:Jitter(Abs) is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:RAP is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:PPQ is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
Jitter:DDP is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
MDVP:Shimmer is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
MDVP:Shimmer(dB) is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
Shimmer:APQ3 is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
Shimmer:APQ5 is highly correlated with MDVP:Jitter(%) and 16 other fieldsHigh correlation
MDVP:APQ is highly correlated with MDVP:Jitter(%) and 16 other fieldsHigh correlation
Shimmer:DDA is highly correlated with MDVP:Jitter(%) and 13 other fieldsHigh correlation
NHR is highly correlated with MDVP:Jitter(%) and 14 other fieldsHigh correlation
HNR is highly correlated with MDVP:Fo(Hz) and 18 other fieldsHigh correlation
status is highly correlated with MDVP:Fo(Hz) and 7 other fieldsHigh correlation
RPDE is highly correlated with HNR and 3 other fieldsHigh correlation
DFA is highly correlated with MDVP:Fo(Hz) and 3 other fieldsHigh correlation
spread1 is highly correlated with MDVP:Fo(Hz) and 20 other fieldsHigh correlation
spread2 is highly correlated with MDVP:Fo(Hz) and 10 other fieldsHigh correlation
D2 is highly correlated with MDVP:Fo(Hz) and 8 other fieldsHigh correlation
PPE is highly correlated with MDVP:Fo(Hz) and 20 other fieldsHigh correlation
name is uniformly distributed Uniform
name has unique values Unique
MDVP:Fo(Hz) has unique values Unique
MDVP:Fhi(Hz) has unique values Unique
MDVP:Flo(Hz) has unique values Unique
HNR has unique values Unique
RPDE has unique values Unique
DFA has unique values Unique
spread1 has unique values Unique
D2 has unique values Unique
PPE has unique values Unique

Reproduction

Analysis started2022-05-17 12:55:07.051369
Analysis finished2022-05-17 12:55:58.645022
Duration51.59 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
phon_R01_S01_1
 
1
phon_R01_S35_1
 
1
phon_R01_S31_3
 
1
phon_R01_S31_4
 
1
phon_R01_S31_5
 
1
Other values (190)
190 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters2730
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)100.0%

Sample

1st rowphon_R01_S01_1
2nd rowphon_R01_S01_2
3rd rowphon_R01_S01_3
4th rowphon_R01_S01_4
5th rowphon_R01_S01_5

Common Values

ValueCountFrequency (%)
phon_R01_S01_11
 
0.5%
phon_R01_S35_11
 
0.5%
phon_R01_S31_31
 
0.5%
phon_R01_S31_41
 
0.5%
phon_R01_S31_51
 
0.5%
phon_R01_S31_61
 
0.5%
phon_R01_S32_11
 
0.5%
phon_R01_S32_21
 
0.5%
phon_R01_S32_31
 
0.5%
phon_R01_S32_41
 
0.5%
Other values (185)185
94.9%

Length

2022-05-17T14:55:58.755613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
phon_r01_s01_11
 
0.5%
phon_r01_s13_21
 
0.5%
phon_r01_s02_61
 
0.5%
phon_r01_s01_31
 
0.5%
phon_r01_s01_41
 
0.5%
phon_r01_s01_51
 
0.5%
phon_r01_s01_61
 
0.5%
phon_r01_s02_11
 
0.5%
phon_r01_s02_21
 
0.5%
phon_r01_s02_31
 
0.5%
Other values (185)185
94.9%

Most occurring characters

ValueCountFrequency (%)
_585
21.4%
1282
10.3%
0255
9.3%
p195
 
7.1%
h195
 
7.1%
S195
 
7.1%
R195
 
7.1%
n195
 
7.1%
o195
 
7.1%
2100
 
3.7%
Other values (7)338
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number975
35.7%
Lowercase Letter780
28.6%
Connector Punctuation585
21.4%
Uppercase Letter390
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1282
28.9%
0255
26.2%
2100
 
10.3%
393
 
9.5%
480
 
8.2%
557
 
5.8%
650
 
5.1%
728
 
2.9%
918
 
1.8%
812
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
p195
25.0%
h195
25.0%
n195
25.0%
o195
25.0%
Uppercase Letter
ValueCountFrequency (%)
S195
50.0%
R195
50.0%
Connector Punctuation
ValueCountFrequency (%)
_585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1560
57.1%
Latin1170
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
_585
37.5%
1282
18.1%
0255
16.3%
2100
 
6.4%
393
 
6.0%
480
 
5.1%
557
 
3.7%
650
 
3.2%
728
 
1.8%
918
 
1.2%
Latin
ValueCountFrequency (%)
p195
16.7%
h195
16.7%
S195
16.7%
R195
16.7%
n195
16.7%
o195
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_585
21.4%
1282
10.3%
0255
9.3%
p195
 
7.1%
h195
 
7.1%
S195
 
7.1%
R195
 
7.1%
n195
 
7.1%
o195
 
7.1%
2100
 
3.7%
Other values (7)338
12.4%

MDVP:Fo(Hz)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.228641
Minimum88.333
Maximum260.105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:55:58.887988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum88.333
5-th percentile101.8791
Q1117.572
median148.79
Q3182.769
95-th percentile236.5078
Maximum260.105
Range171.772
Interquartile range (IQR)65.197

Descriptive statistics

Standard deviation41.39006475
Coefficient of variation (CV)0.2683682128
Kurtosis-0.6278981067
Mean154.228641
Median Absolute Deviation (MAD)31.786
Skewness0.5917374637
Sum30074.585
Variance1713.13746
MonotonicityNot monotonic
2022-05-17T14:55:59.019332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.9921
 
0.5%
169.7741
 
0.5%
156.2391
 
0.5%
145.1741
 
0.5%
138.1451
 
0.5%
166.8881
 
0.5%
119.0311
 
0.5%
120.0781
 
0.5%
120.2891
 
0.5%
120.2561
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
88.3331
0.5%
91.9041
0.5%
95.0561
0.5%
95.3851
0.5%
95.6051
0.5%
95.731
0.5%
96.1061
0.5%
98.8041
0.5%
100.771
0.5%
100.961
0.5%
ValueCountFrequency (%)
260.1051
0.5%
252.4551
0.5%
245.511
0.5%
244.991
0.5%
243.4391
0.5%
242.8521
0.5%
241.4041
0.5%
240.3011
0.5%
237.3231
0.5%
237.2261
0.5%

MDVP:Fhi(Hz)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.1049179
Minimum102.145
Maximum592.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:55:59.146067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum102.145
5-th percentile115.8188
Q1134.8625
median175.829
Q3224.2055
95-th percentile410.6398
Maximum592.03
Range489.885
Interquartile range (IQR)89.343

Descriptive statistics

Standard deviation91.49154764
Coefficient of variation (CV)0.4641768891
Kurtosis7.627241212
Mean197.1049179
Median Absolute Deviation (MAD)42.485
Skewness2.542145998
Sum38435.459
Variance8370.703289
MonotonicityNot monotonic
2022-05-17T14:55:59.275953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3021
 
0.5%
191.7591
 
0.5%
195.1071
 
0.5%
198.1091
 
0.5%
197.2381
 
0.5%
198.9661
 
0.5%
127.5331
 
0.5%
126.6321
 
0.5%
128.1431
 
0.5%
125.3061
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
102.1451
0.5%
102.3051
0.5%
107.7151
0.5%
108.6641
0.5%
110.0191
0.5%
112.241
0.5%
112.7771
0.5%
113.5971
0.5%
113.841
0.5%
115.6971
0.5%
ValueCountFrequency (%)
592.031
0.5%
588.5181
0.5%
586.5671
0.5%
581.2891
0.5%
565.741
0.5%
492.8921
0.5%
479.6971
0.5%
450.2471
0.5%
442.8241
0.5%
442.5571
0.5%

MDVP:Flo(Hz)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.3246308
Minimum65.476
Maximum239.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:55:59.397698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum65.476
5-th percentile68.9464
Q184.291
median104.315
Q3140.0185
95-th percentile220.1949
Maximum239.17
Range173.694
Interquartile range (IQR)55.7275

Descriptive statistics

Standard deviation43.52141318
Coefficient of variation (CV)0.3741375571
Kurtosis0.6546145211
Mean116.3246308
Median Absolute Deviation (MAD)23.678
Skewness1.217350449
Sum22683.303
Variance1894.113405
MonotonicityNot monotonic
2022-05-17T14:55:59.519268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.9971
 
0.5%
151.4511
 
0.5%
79.821
 
0.5%
80.6371
 
0.5%
81.1141
 
0.5%
79.5121
 
0.5%
109.2161
 
0.5%
105.6671
 
0.5%
100.2091
 
0.5%
104.7731
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
65.4761
0.5%
65.751
0.5%
65.7821
0.5%
65.8091
0.5%
66.0041
0.5%
66.1571
0.5%
67.0211
0.5%
67.3431
0.5%
68.4011
0.5%
68.6231
0.5%
ValueCountFrequency (%)
239.171
0.5%
237.3031
0.5%
232.4831
0.5%
232.4351
0.5%
231.8481
0.5%
229.2561
0.5%
227.9111
0.5%
225.2271
0.5%
223.6341
0.5%
221.1561
0.5%

MDVP:Jitter(%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct173
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006220461538
Minimum0.00168
Maximum0.03316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:55:59.647296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00168
5-th percentile0.002211
Q10.00346
median0.00494
Q30.007365
95-th percentile0.015561
Maximum0.03316
Range0.03148
Interquartile range (IQR)0.003905

Descriptive statistics

Standard deviation0.004848133693
Coefficient of variation (CV)0.7793848837
Kurtosis12.03093928
Mean0.006220461538
Median Absolute Deviation (MAD)0.0018
Skewness3.084946201
Sum1.21299
Variance2.35044003 × 10-5
MonotonicityNot monotonic
2022-05-17T14:55:59.767724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006943
 
1.5%
0.007423
 
1.5%
0.003693
 
1.5%
0.007842
 
1.0%
0.003142
 
1.0%
0.004482
 
1.0%
0.004512
 
1.0%
0.003462
 
1.0%
0.002582
 
1.0%
0.002982
 
1.0%
Other values (163)172
88.2%
ValueCountFrequency (%)
0.001681
0.5%
0.001741
0.5%
0.001781
0.5%
0.00181
0.5%
0.001831
0.5%
0.001851
0.5%
0.001981
0.5%
0.002051
0.5%
0.00211
0.5%
0.002121
0.5%
ValueCountFrequency (%)
0.033161
0.5%
0.031071
0.5%
0.030111
0.5%
0.027141
0.5%
0.019361
0.5%
0.018721
0.5%
0.018131
0.5%
0.017191
0.5%
0.016271
0.5%
0.015681
0.5%

MDVP:Jitter(Abs)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.395897436 × 10-5
Minimum7 × 10-6
Maximum0.00026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:55:59.875204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7 × 10-6
5-th percentile1 × 10-5
Q12 × 10-5
median3 × 10-5
Q36 × 10-5
95-th percentile0.0001
Maximum0.00026
Range0.000253
Interquartile range (IQR)4 × 10-5

Descriptive statistics

Standard deviation3.48219086 × 10-5
Coefficient of variation (CV)0.7921456109
Kurtosis10.86904252
Mean4.395897436 × 10-5
Median Absolute Deviation (MAD)1 × 10-5
Skewness2.649071417
Sum0.008572
Variance1.212565319 × 10-9
MonotonicityNot monotonic
2022-05-17T14:55:59.974483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 × 10-546
23.6%
4 × 10-528
14.4%
2 × 10-528
14.4%
1 × 10-520
10.3%
5 × 10-517
 
8.7%
6 × 10-516
 
8.2%
8 × 10-59
 
4.6%
7 × 10-58
 
4.1%
9 × 10-55
 
2.6%
9 × 10-65
 
2.6%
Other values (9)13
 
6.7%
ValueCountFrequency (%)
7 × 10-61
 
0.5%
9 × 10-65
 
2.6%
1 × 10-520
10.3%
2 × 10-528
14.4%
3 × 10-546
23.6%
4 × 10-528
14.4%
5 × 10-517
 
8.7%
6 × 10-516
 
8.2%
7 × 10-58
 
4.1%
8 × 10-59
 
4.6%
ValueCountFrequency (%)
0.000261
 
0.5%
0.000221
 
0.5%
0.000161
 
0.5%
0.000152
 
1.0%
0.000141
 
0.5%
0.000121
 
0.5%
0.000112
 
1.0%
0.00013
 
1.5%
9 × 10-55
2.6%
8 × 10-59
4.6%

MDVP:RAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct155
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003306410256
Minimum0.00068
Maximum0.02144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:00.095434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00068
5-th percentile0.001118
Q10.00166
median0.0025
Q30.003835
95-th percentile0.008756
Maximum0.02144
Range0.02076
Interquartile range (IQR)0.002175

Descriptive statistics

Standard deviation0.002967774416
Coefficient of variation (CV)0.8975820258
Kurtosis14.21379772
Mean0.003306410256
Median Absolute Deviation (MAD)0.00098
Skewness3.36070845
Sum0.64475
Variance8.807684985 × 10-6
MonotonicityNot monotonic
2022-05-17T14:56:00.222723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001695
 
2.6%
0.001343
 
1.5%
0.004283
 
1.5%
0.00253
 
1.5%
0.001573
 
1.5%
0.001653
 
1.5%
0.00372
 
1.0%
0.002442
 
1.0%
0.003162
 
1.0%
0.003312
 
1.0%
Other values (145)167
85.6%
ValueCountFrequency (%)
0.000681
0.5%
0.000751
0.5%
0.000761
0.5%
0.000921
0.5%
0.000931
0.5%
0.000941
0.5%
0.0011
0.5%
0.001052
1.0%
0.001091
0.5%
0.001131
0.5%
ValueCountFrequency (%)
0.021441
0.5%
0.018541
0.5%
0.0181
0.5%
0.015681
0.5%
0.011591
0.5%
0.011171
0.5%
0.010751
0.5%
0.009961
0.5%
0.009191
0.5%
0.009051
0.5%

MDVP:PPQ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct165
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003446358974
Minimum0.00092
Maximum0.01958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:00.350395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00092
5-th percentile0.001315
Q10.00186
median0.00269
Q30.003955
95-th percentile0.009083
Maximum0.01958
Range0.01866
Interquartile range (IQR)0.002095

Descriptive statistics

Standard deviation0.002758976647
Coefficient of variation (CV)0.8005482503
Kurtosis11.96392212
Mean0.003446358974
Median Absolute Deviation (MAD)0.00094
Skewness3.073892458
Sum0.67204
Variance7.611952139 × 10-6
MonotonicityNot monotonic
2022-05-17T14:56:00.482288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003324
 
2.1%
0.002833
 
1.5%
0.002033
 
1.5%
0.001823
 
1.5%
0.001132
 
1.0%
0.001942
 
1.0%
0.001922
 
1.0%
0.003122
 
1.0%
0.002582
 
1.0%
0.00392
 
1.0%
Other values (155)170
87.2%
ValueCountFrequency (%)
0.000921
0.5%
0.000961
0.5%
0.0011
0.5%
0.001061
0.5%
0.001071
0.5%
0.001132
1.0%
0.001151
0.5%
0.001221
0.5%
0.001281
0.5%
0.001331
0.5%
ValueCountFrequency (%)
0.019581
0.5%
0.016991
0.5%
0.016281
0.5%
0.015221
0.5%
0.011541
0.5%
0.010271
0.5%
0.00991
0.5%
0.009631
0.5%
0.009461
0.5%
0.009091
0.5%

Jitter:DDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct180
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009919948718
Minimum0.00204
Maximum0.06433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:00.624457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00204
5-th percentile0.003354
Q10.004985
median0.00749
Q30.011505
95-th percentile0.026271
Maximum0.06433
Range0.06229
Interquartile range (IQR)0.00652

Descriptive statistics

Standard deviation0.008903344356
Coefficient of variation (CV)0.8975191918
Kurtosis14.22476191
Mean0.009919948718
Median Absolute Deviation (MAD)0.00293
Skewness3.362058448
Sum1.93439
Variance7.926954072 × 10-5
MonotonicityNot monotonic
2022-05-17T14:56:00.750185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005073
 
1.5%
0.011092
 
1.0%
0.00782
 
1.0%
0.00752
 
1.0%
0.007312
 
1.0%
0.009942
 
1.0%
0.006962
 
1.0%
0.012852
 
1.0%
0.006162
 
1.0%
0.004962
 
1.0%
Other values (170)174
89.2%
ValueCountFrequency (%)
0.002041
0.5%
0.002251
0.5%
0.002291
0.5%
0.002761
0.5%
0.002781
0.5%
0.002831
0.5%
0.003011
0.5%
0.003141
0.5%
0.003151
0.5%
0.003271
0.5%
ValueCountFrequency (%)
0.064331
0.5%
0.055631
0.5%
0.054011
0.5%
0.047051
0.5%
0.034761
0.5%
0.033511
0.5%
0.032251
0.5%
0.029871
0.5%
0.027561
0.5%
0.027161
0.5%

MDVP:Shimmer
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct188
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02970912821
Minimum0.00954
Maximum0.11908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:00.880496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00954
5-th percentile0.011211
Q10.016505
median0.02297
Q30.037885
95-th percentile0.067256
Maximum0.11908
Range0.10954
Interquartile range (IQR)0.02138

Descriptive statistics

Standard deviation0.01885693186
Coefficient of variation (CV)0.6347184518
Kurtosis3.238308111
Mean0.02970912821
Median Absolute Deviation (MAD)0.00839
Skewness1.66648041
Sum5.79328
Variance0.0003555838791
MonotonicityNot monotonic
2022-05-17T14:56:01.002535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024482
 
1.0%
0.032732
 
1.0%
0.016082
 
1.0%
0.017252
 
1.0%
0.022932
 
1.0%
0.01452
 
1.0%
0.015032
 
1.0%
0.014121
 
0.5%
0.044791
 
0.5%
0.025031
 
0.5%
Other values (178)178
91.3%
ValueCountFrequency (%)
0.009541
0.5%
0.009581
0.5%
0.010151
0.5%
0.010221
0.5%
0.010241
0.5%
0.01031
0.5%
0.010331
0.5%
0.010431
0.5%
0.010641
0.5%
0.010981
0.5%
ValueCountFrequency (%)
0.119081
0.5%
0.094191
0.5%
0.091781
0.5%
0.086841
0.5%
0.081431
0.5%
0.079591
0.5%
0.07171
0.5%
0.071181
0.5%
0.067341
0.5%
0.067271
0.5%

MDVP:Shimmer(dB)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct149
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2822512821
Minimum0.085
Maximum1.302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:01.124123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile0.1018
Q10.1485
median0.221
Q30.35
95-th percentile0.6527
Maximum1.302
Range1.217
Interquartile range (IQR)0.2015

Descriptive statistics

Standard deviation0.1948772901
Coefficient of variation (CV)0.6904389898
Kurtosis5.12819251
Mean0.2822512821
Median Absolute Deviation (MAD)0.086
Skewness1.999388639
Sum55.039
Variance0.03797715818
MonotonicityNot monotonic
2022-05-17T14:56:01.255343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1545
 
2.6%
0.1974
 
2.1%
0.1553
 
1.5%
0.1263
 
1.5%
0.1293
 
1.5%
0.1453
 
1.5%
0.3073
 
1.5%
0.2553
 
1.5%
0.0852
 
1.0%
0.1072
 
1.0%
Other values (139)164
84.1%
ValueCountFrequency (%)
0.0852
1.0%
0.0891
0.5%
0.091
0.5%
0.0931
0.5%
0.0941
0.5%
0.0972
1.0%
0.0981
0.5%
0.0991
0.5%
0.1031
0.5%
0.1062
1.0%
ValueCountFrequency (%)
1.3021
0.5%
1.0181
0.5%
0.931
0.5%
0.8911
0.5%
0.8331
0.5%
0.8211
0.5%
0.7841
0.5%
0.7721
0.5%
0.7221
0.5%
0.6591
0.5%

Shimmer:APQ3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct184
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01566415385
Minimum0.00455
Maximum0.05647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:01.380820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00455
5-th percentile0.005368
Q10.008245
median0.01279
Q30.020265
95-th percentile0.036227
Maximum0.05647
Range0.05192
Interquartile range (IQR)0.01202

Descriptive statistics

Standard deviation0.0101531616
Coefficient of variation (CV)0.6481781075
Kurtosis2.72015164
Mean0.01566415385
Median Absolute Deviation (MAD)0.0051
Skewness1.58057638
Sum3.05451
Variance0.0001030866904
MonotonicityNot monotonic
2022-05-17T14:56:01.512275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.014412
 
1.0%
0.008292
 
1.0%
0.012772
 
1.0%
0.014842
 
1.0%
0.007282
 
1.0%
0.004692
 
1.0%
0.015792
 
1.0%
0.006332
 
1.0%
0.012842
 
1.0%
0.005222
 
1.0%
Other values (174)175
89.7%
ValueCountFrequency (%)
0.004551
0.5%
0.004681
0.5%
0.004692
1.0%
0.004761
0.5%
0.00491
0.5%
0.005041
0.5%
0.005222
1.0%
0.005341
0.5%
0.005381
0.5%
0.005571
0.5%
ValueCountFrequency (%)
0.056471
0.5%
0.055511
0.5%
0.053581
0.5%
0.044211
0.5%
0.042841
0.5%
0.040161
0.5%
0.038041
0.5%
0.037881
0.5%
0.036711
0.5%
0.03651
0.5%

Shimmer:APQ5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct189
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01787825641
Minimum0.0057
Maximum0.0794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:01.643246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0057
5-th percentile0.006383
Q10.00958
median0.01347
Q30.02238
95-th percentile0.042701
Maximum0.0794
Range0.0737
Interquartile range (IQR)0.0128

Descriptive statistics

Standard deviation0.01202370554
Coefficient of variation (CV)0.6725323355
Kurtosis3.874209652
Mean0.01787825641
Median Absolute Deviation (MAD)0.00468
Skewness1.798697067
Sum3.48626
Variance0.0001445694949
MonotonicityNot monotonic
2022-05-17T14:56:01.772429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012192
 
1.0%
0.010242
 
1.0%
0.007472
 
1.0%
0.009722
 
1.0%
0.01162
 
1.0%
0.011442
 
1.0%
0.006211
 
0.5%
0.018051
 
0.5%
0.018591
 
0.5%
0.00571
 
0.5%
Other values (179)179
91.8%
ValueCountFrequency (%)
0.00571
0.5%
0.005761
0.5%
0.005821
0.5%
0.005881
0.5%
0.006061
0.5%
0.00611
0.5%
0.006211
0.5%
0.00631
0.5%
0.006311
0.5%
0.006321
0.5%
ValueCountFrequency (%)
0.07941
0.5%
0.055561
0.5%
0.054261
0.5%
0.050051
0.5%
0.049621
0.5%
0.048251
0.5%
0.047911
0.5%
0.04581
0.5%
0.045181
0.5%
0.042821
0.5%

MDVP:APQ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct189
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02408148718
Minimum0.00719
Maximum0.13778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:01.897848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00719
5-th percentile0.009114
Q10.01308
median0.01826
Q30.0294
95-th percentile0.057718
Maximum0.13778
Range0.13059
Interquartile range (IQR)0.01632

Descriptive statistics

Standard deviation0.01694673625
Coefficient of variation (CV)0.7037246546
Kurtosis11.16328838
Mean0.02408148718
Median Absolute Deviation (MAD)0.00636
Skewness2.618046502
Sum4.69589
Variance0.0002871918694
MonotonicityNot monotonic
2022-05-17T14:56:02.027579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012342
 
1.0%
0.037722
 
1.0%
0.009032
 
1.0%
0.014912
 
1.0%
0.011332
 
1.0%
0.01142
 
1.0%
0.033161
 
0.5%
0.022591
 
0.5%
0.023011
 
0.5%
0.008111
 
0.5%
Other values (179)179
91.8%
ValueCountFrequency (%)
0.007191
0.5%
0.007261
0.5%
0.007621
0.5%
0.008021
0.5%
0.008111
0.5%
0.00861
0.5%
0.008711
0.5%
0.008821
0.5%
0.009032
1.0%
0.009151
0.5%
ValueCountFrequency (%)
0.137781
0.5%
0.088081
0.5%
0.083181
0.5%
0.068241
0.5%
0.06461
0.5%
0.063591
0.5%
0.062591
0.5%
0.061961
0.5%
0.060231
0.5%
0.057831
0.5%

Shimmer:DDA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct189
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04699261538
Minimum0.01364
Maximum0.16942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:02.153116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01364
5-th percentile0.016107
Q10.024735
median0.03836
Q30.060795
95-th percentile0.108678
Maximum0.16942
Range0.15578
Interquartile range (IQR)0.03606

Descriptive statistics

Standard deviation0.03045911943
Coefficient of variation (CV)0.6481682107
Kurtosis2.720661344
Mean0.04699261538
Median Absolute Deviation (MAD)0.01529
Skewness1.580617994
Sum9.16356
Variance0.0009277579565
MonotonicityNot monotonic
2022-05-17T14:56:02.284125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.021842
 
1.0%
0.044512
 
1.0%
0.015672
 
1.0%
0.047362
 
1.0%
0.018982
 
1.0%
0.038312
 
1.0%
0.014711
 
0.5%
0.038671
 
0.5%
0.037061
 
0.5%
0.046411
 
0.5%
Other values (179)179
91.8%
ValueCountFrequency (%)
0.013641
0.5%
0.014031
0.5%
0.014061
0.5%
0.014071
0.5%
0.014281
0.5%
0.014711
0.5%
0.015131
0.5%
0.015672
1.0%
0.016031
0.5%
0.016141
0.5%
ValueCountFrequency (%)
0.169421
0.5%
0.166541
0.5%
0.160741
0.5%
0.132621
0.5%
0.128511
0.5%
0.120471
0.5%
0.114111
0.5%
0.113631
0.5%
0.110121
0.5%
0.109491
0.5%

NHR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct185
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02484707692
Minimum0.00065
Maximum0.31482
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:02.415817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00065
5-th percentile0.002528
Q10.005925
median0.01166
Q30.02564
95-th percentile0.092044
Maximum0.31482
Range0.31417
Interquartile range (IQR)0.019715

Descriptive statistics

Standard deviation0.04041844856
Coefficient of variation (CV)1.626688269
Kurtosis21.99497411
Mean0.02484707692
Median Absolute Deviation (MAD)0.0069
Skewness4.220709129
Sum4.84518
Variance0.001633650984
MonotonicityNot monotonic
2022-05-17T14:56:02.543263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.072232
 
1.0%
0.004762
 
1.0%
0.008392
 
1.0%
0.002312
 
1.0%
0.00622
 
1.0%
0.00422
 
1.0%
0.00342
 
1.0%
0.006812
 
1.0%
0.004792
 
1.0%
0.010492
 
1.0%
Other values (175)175
89.7%
ValueCountFrequency (%)
0.000651
0.5%
0.000721
0.5%
0.001191
0.5%
0.001351
0.5%
0.001671
0.5%
0.002312
1.0%
0.002331
0.5%
0.002381
0.5%
0.002431
0.5%
0.002571
0.5%
ValueCountFrequency (%)
0.314821
0.5%
0.25931
0.5%
0.217131
0.5%
0.167441
0.5%
0.162651
0.5%
0.118431
0.5%
0.109521
0.5%
0.107481
0.5%
0.107151
0.5%
0.103231
0.5%

HNR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.88597436
Minimum8.441
Maximum33.047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:02.669085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8.441
5-th percentile13.4838
Q119.198
median22.085
Q325.0755
95-th percentile26.9742
Maximum33.047
Range24.606
Interquartile range (IQR)5.8775

Descriptive statistics

Standard deviation4.425764269
Coefficient of variation (CV)0.202219202
Kurtosis0.6160358344
Mean21.88597436
Median Absolute Deviation (MAD)2.945
Skewness-0.5143174976
Sum4267.765
Variance19.58738937
MonotonicityNot monotonic
2022-05-17T14:56:02.799310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.0331
 
0.5%
12.3591
 
0.5%
19.1961
 
0.5%
18.8571
 
0.5%
18.1781
 
0.5%
18.331
 
0.5%
26.8421
 
0.5%
26.3691
 
0.5%
23.9491
 
0.5%
26.0171
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
8.4411
0.5%
8.8671
0.5%
9.4491
0.5%
10.4891
0.5%
11.7441
0.5%
11.8661
0.5%
12.2981
0.5%
12.3591
0.5%
12.4351
0.5%
12.5291
0.5%
ValueCountFrequency (%)
33.0471
0.5%
32.6841
0.5%
31.7321
0.5%
30.941
0.5%
30.7751
0.5%
29.9281
0.5%
29.7461
0.5%
28.4091
0.5%
27.4211
0.5%
27.1661
0.5%

status
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1
147 
0
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1147
75.4%
048
 
24.6%

Length

2022-05-17T14:56:02.908700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-17T14:56:03.063996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1147
75.4%
048
 
24.6%

Most occurring characters

ValueCountFrequency (%)
1147
75.4%
048
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1147
75.4%
048
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1147
75.4%
048
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1147
75.4%
048
 
24.6%

RPDE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4985355385
Minimum0.25657
Maximum0.685151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:03.164689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.25657
5-th percentile0.3309287
Q10.421306
median0.495954
Q30.5875625
95-th percentile0.6532203
Maximum0.685151
Range0.428581
Interquartile range (IQR)0.1662565

Descriptive statistics

Standard deviation0.1039417141
Coefficient of variation (CV)0.2084940914
Kurtosis-0.9217809778
Mean0.4985355385
Median Absolute Deviation (MAD)0.082659
Skewness-0.1434024138
Sum97.21443
Variance0.01080387994
MonotonicityNot monotonic
2022-05-17T14:56:03.291563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4147831
 
0.5%
0.561611
 
0.5%
0.6186631
 
0.5%
0.6375181
 
0.5%
0.6232091
 
0.5%
0.5851691
 
0.5%
0.4575411
 
0.5%
0.4913451
 
0.5%
0.467161
 
0.5%
0.4686211
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
0.256571
0.5%
0.2636541
0.5%
0.276851
0.5%
0.2968881
0.5%
0.3050621
0.5%
0.3054291
0.5%
0.3064431
0.5%
0.3113691
0.5%
0.326481
0.5%
0.3295771
0.5%
ValueCountFrequency (%)
0.6851511
0.5%
0.6771311
0.5%
0.6713781
0.5%
0.6712991
0.5%
0.6653181
0.5%
0.6638421
0.5%
0.6601251
0.5%
0.6549451
0.5%
0.6534271
0.5%
0.653411
0.5%

DFA
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7180990462
Minimum0.574282
Maximum0.825288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:03.913590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.574282
5-th percentile0.6323376
Q10.6747575
median0.722254
Q30.7618815
95-th percentile0.8160376
Maximum0.825288
Range0.251006
Interquartile range (IQR)0.087124

Descriptive statistics

Standard deviation0.05533583035
Coefficient of variation (CV)0.07705877155
Kurtosis-0.6861518493
Mean0.7180990462
Median Absolute Deviation (MAD)0.043369
Skewness-0.03321366071
Sum140.029314
Variance0.00306205412
MonotonicityNot monotonic
2022-05-17T14:56:04.038556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8152851
 
0.5%
0.7935091
 
0.5%
0.7284211
 
0.5%
0.7355461
 
0.5%
0.7382451
 
0.5%
0.7369641
 
0.5%
0.6997871
 
0.5%
0.7188391
 
0.5%
0.7240451
 
0.5%
0.7351361
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
0.5742821
0.5%
0.582711
0.5%
0.6054171
0.5%
0.6237311
0.5%
0.626711
0.5%
0.6273371
0.5%
0.6280581
0.5%
0.6282321
0.5%
0.6304091
0.5%
0.6316531
0.5%
ValueCountFrequency (%)
0.8252881
0.5%
0.8250691
0.5%
0.8234841
0.5%
0.8213641
0.5%
0.8195211
0.5%
0.8192351
0.5%
0.8190321
0.5%
0.8177561
0.5%
0.8173961
0.5%
0.816341
0.5%

spread1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.684396744
Minimum-7.964984
Maximum-2.434031
Zeros0
Zeros (%)0.0%
Negative195
Negative (%)100.0%
Memory size1.6 KiB
2022-05-17T14:56:04.161973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7.964984
5-th percentile-7.306315
Q1-6.450096
median-5.720868
Q3-5.046192
95-th percentile-3.7336141
Maximum-2.434031
Range5.530953
Interquartile range (IQR)1.403904

Descriptive statistics

Standard deviation1.090207764
Coefficient of variation (CV)-0.1917895272
Kurtosis-0.05019918161
Mean-5.684396744
Median Absolute Deviation (MAD)0.71853
Skewness0.432138932
Sum-1108.457365
Variance1.188552968
MonotonicityNot monotonic
2022-05-17T14:56:04.282757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.8130311
 
0.5%
-3.2976681
 
0.5%
-5.9441911
 
0.5%
-5.5942751
 
0.5%
-5.5403511
 
0.5%
-5.8252571
 
0.5%
-6.8900211
 
0.5%
-5.8920611
 
0.5%
-6.1352961
 
0.5%
-6.1126671
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
-7.9649841
0.5%
-7.7776851
0.5%
-7.6957341
0.5%
-7.6825871
0.5%
-7.5179341
0.5%
-7.4962641
0.5%
-7.34831
0.5%
-7.319511
0.5%
-7.3142371
0.5%
-7.310551
0.5%
ValueCountFrequency (%)
-2.4340311
0.5%
-2.8397561
0.5%
-2.9293791
0.5%
-2.931071
0.5%
-3.2694871
0.5%
-3.2976681
0.5%
-3.3773251
0.5%
-3.4444781
0.5%
-3.5837221
0.5%
-3.7005441
0.5%

spread2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct194
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2265103487
Minimum0.006274
Maximum0.450493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:04.412621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.006274
5-th percentile0.0888389
Q10.1743505
median0.218885
Q30.279234
95-th percentile0.3731391
Maximum0.450493
Range0.444219
Interquartile range (IQR)0.1048835

Descriptive statistics

Standard deviation0.08340576262
Coefficient of variation (CV)0.3682205387
Kurtosis-0.08302289328
Mean0.2265103487
Median Absolute Deviation (MAD)0.048702
Skewness0.1444304855
Sum44.169518
Variance0.006956521238
MonotonicityNot monotonic
2022-05-17T14:56:04.544070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2102792
 
1.0%
0.2664821
 
0.5%
0.3203851
 
0.5%
0.127951
 
0.5%
0.0871651
 
0.5%
0.1156971
 
0.5%
0.1529411
 
0.5%
0.1959761
 
0.5%
0.203631
 
0.5%
0.2170131
 
0.5%
Other values (184)184
94.4%
ValueCountFrequency (%)
0.0062741
0.5%
0.0186891
0.5%
0.0568441
0.5%
0.0634121
0.5%
0.0669941
0.5%
0.0732981
0.5%
0.0782021
0.5%
0.0863721
0.5%
0.0871651
0.5%
0.087841
0.5%
ValueCountFrequency (%)
0.4504931
0.5%
0.4343261
0.5%
0.4147581
0.5%
0.3977491
0.5%
0.3967461
0.5%
0.3930561
0.5%
0.3910021
0.5%
0.3892951
0.5%
0.3892321
0.5%
0.3755311
0.5%

D2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.381826087
Minimum1.423287
Maximum3.671155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:04.671248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.423287
5-th percentile1.8487408
Q12.0991255
median2.361532
Q32.636456
95-th percentile3.0849315
Maximum3.671155
Range2.247868
Interquartile range (IQR)0.5373305

Descriptive statistics

Standard deviation0.3827990465
Coefficient of variation (CV)0.1607166235
Kurtosis0.2203341048
Mean2.381826087
Median Absolute Deviation (MAD)0.271094
Skewness0.4303838913
Sum464.456087
Variance0.14653511
MonotonicityNot monotonic
2022-05-17T14:56:04.793178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3014421
 
0.5%
3.4136491
 
0.5%
1.9297151
 
0.5%
1.7659571
 
0.5%
1.8212971
 
0.5%
1.9961461
 
0.5%
2.3285131
 
0.5%
2.1088731
 
0.5%
2.5397241
 
0.5%
2.5277421
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
1.4232871
0.5%
1.5122751
0.5%
1.5446091
0.5%
1.7438671
0.5%
1.7659571
0.5%
1.7779011
0.5%
1.8212971
0.5%
1.8270121
0.5%
1.8316911
0.5%
1.8401981
0.5%
ValueCountFrequency (%)
3.6711551
0.5%
3.4136491
0.5%
3.3175861
0.5%
3.2748651
0.5%
3.1840271
0.5%
3.1423641
0.5%
3.136551
0.5%
3.109011
0.5%
3.0993011
0.5%
3.0982561
0.5%

PPE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.206551641
Minimum0.044539
Maximum0.527367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-05-17T14:56:04.917291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.044539
5-th percentile0.0915866
Q10.137451
median0.194052
Q30.25298
95-th percentile0.3695708
Maximum0.527367
Range0.482828
Interquartile range (IQR)0.115529

Descriptive statistics

Standard deviation0.09011932248
Coefficient of variation (CV)0.4363040741
Kurtosis0.5283349473
Mean0.206551641
Median Absolute Deviation (MAD)0.058352
Skewness0.7974910716
Sum40.27757
Variance0.008121492285
MonotonicityNot monotonic
2022-05-17T14:56:05.046238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2846541
 
0.5%
0.4575331
 
0.5%
0.1819881
 
0.5%
0.2227161
 
0.5%
0.2140751
 
0.5%
0.1965351
 
0.5%
0.1128561
 
0.5%
0.1835721
 
0.5%
0.1699231
 
0.5%
0.1706331
 
0.5%
Other values (185)185
94.9%
ValueCountFrequency (%)
0.0445391
0.5%
0.0561411
0.5%
0.057611
0.5%
0.0685011
0.5%
0.0735811
0.5%
0.0755871
0.5%
0.0855691
0.5%
0.0863981
0.5%
0.091471
0.5%
0.0915461
0.5%
ValueCountFrequency (%)
0.5273671
0.5%
0.4575331
0.5%
0.4547211
0.5%
0.4447741
0.5%
0.4307881
0.5%
0.4186461
0.5%
0.4103351
0.5%
0.3784831
0.5%
0.3774291
0.5%
0.3709611
0.5%

Interactions

2022-05-17T14:55:55.981936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:10.329751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:12.530518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:14.728065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:16.865848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:19.128116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:21.231604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:23.438565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:25.690100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:27.991880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:30.073423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:32.140836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:34.397256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:36.577702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:38.712072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:40.891895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:43.325917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:45.365315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:47.423484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:49.457011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:51.522985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:53.949913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:56.072715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:10.477045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:12.626114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:14.823455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:17.087755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:19.223515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:21.325706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:23.542676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:25.787624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:28.087495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:30.167301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:32.231780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:34.496372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:36.676852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:38.812712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:40.989802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:43.420990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:45.459174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-05-17T14:55:57.814097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:12.339975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:14.548371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:16.680854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:18.946875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:21.048579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:23.258945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:25.491764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:27.807989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:29.891799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:31.961416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:34.224020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:36.387830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:38.529600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:40.704366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:43.143942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:45.186841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:47.244826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:49.277923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:51.344879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:53.774996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:55.806791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:57.902112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:12.439361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:14.636947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:16.772621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:19.037124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:21.142564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:23.350929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:25.592995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:27.896654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:29.981291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:32.049683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:34.309345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:36.480508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:38.620348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:40.799516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:43.233686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:45.275912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:47.333272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:49.372322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:51.433513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:53.860793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-17T14:55:55.893488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-17T14:56:05.188205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-17T14:56:05.405276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-17T14:56:05.623108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-17T14:56:05.828418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-17T14:55:58.124059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-17T14:55:58.509578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

nameMDVP:Fo(Hz)MDVP:Fhi(Hz)MDVP:Flo(Hz)MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)Shimmer:APQ3Shimmer:APQ5MDVP:APQShimmer:DDANHRHNRstatusRPDEDFAspread1spread2D2PPE
0phon_R01_S01_1119.992157.30274.9970.007840.000070.003700.005540.011090.043740.4260.021820.031300.029710.065450.0221121.03310.4147830.815285-4.8130310.2664822.3014420.284654
1phon_R01_S01_2122.400148.650113.8190.009680.000080.004650.006960.013940.061340.6260.031340.045180.043680.094030.0192919.08510.4583590.819521-4.0751920.3355902.4868550.368674
2phon_R01_S01_3116.682131.111111.5550.010500.000090.005440.007810.016330.052330.4820.027570.038580.035900.082700.0130920.65110.4298950.825288-4.4431790.3111732.3422590.332634
3phon_R01_S01_4116.676137.871111.3660.009970.000090.005020.006980.015050.054920.5170.029240.040050.037720.087710.0135320.64410.4349690.819235-4.1175010.3341472.4055540.368975
4phon_R01_S01_5116.014141.781110.6550.012840.000110.006550.009080.019660.064250.5840.034900.048250.044650.104700.0176719.64910.4173560.823484-3.7477870.2345132.3321800.410335
5phon_R01_S01_6120.552131.162113.7870.009680.000080.004630.007500.013880.047010.4560.023280.035260.032430.069850.0122221.37810.4155640.825069-4.2428670.2991112.1875600.357775
6phon_R01_S02_1120.267137.244114.8200.003330.000030.001550.002020.004660.016080.1400.007790.009370.013510.023370.0060724.88610.5960400.764112-5.6343220.2576821.8547850.211756
7phon_R01_S02_2107.332113.840104.3150.002900.000030.001440.001820.004310.015670.1340.008290.009460.012560.024870.0034426.89210.6374200.763262-6.1676030.1837212.0646930.163755
8phon_R01_S02_395.730132.06891.7540.005510.000060.002930.003320.008800.020930.1910.010730.012770.017170.032180.0107021.81210.6155510.773587-5.4986780.3277692.3225110.231571
9phon_R01_S02_495.056120.10391.2260.005320.000060.002680.003320.008030.028380.2550.014410.017250.024440.043240.0102221.86210.5470370.798463-5.0118790.3259962.4327920.271362

Last rows

nameMDVP:Fo(Hz)MDVP:Fhi(Hz)MDVP:Flo(Hz)MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)Shimmer:APQ3Shimmer:APQ5MDVP:APQShimmer:DDANHRHNRstatusRPDEDFAspread1spread2D2PPE
185phon_R01_S49_3116.286177.29196.9830.003140.000030.001340.001920.004030.015640.1360.006670.009900.016910.020010.0073724.19900.5985150.654331-5.5925840.1339172.0586580.214346
186phon_R01_S49_4116.556592.03086.2280.004960.000040.002540.002630.007620.016600.1540.008200.009720.014910.024600.0139723.95800.5664240.667654-6.4311190.1533102.1619360.120605
187phon_R01_S49_5116.342581.28994.2460.002670.000020.001150.001480.003450.013000.1170.006310.007890.011440.018920.0068025.02300.5284850.663884-6.3590180.1166362.1520830.138868
188phon_R01_S49_6114.563119.16786.6470.003270.000030.001460.001840.004390.011850.1060.005570.007210.010950.016720.0070324.77500.5553030.659132-6.7102190.1496941.9139900.121777
189phon_R01_S50_1201.774262.70778.2280.006940.000030.004120.003960.012350.025740.2550.014540.015820.017580.043630.0444119.36800.5084790.683761-6.9344740.1598902.3163460.112838
190phon_R01_S50_2174.188230.97894.2610.004590.000030.002630.002590.007900.040870.4050.023360.024980.027450.070080.0276419.51700.4484390.657899-6.5385860.1219522.6574760.133050
191phon_R01_S50_3209.516253.01789.4880.005640.000030.003310.002920.009940.027510.2630.016040.016570.018790.048120.0181019.14700.4316740.683244-6.1953250.1293032.7843120.168895
192phon_R01_S50_4174.688240.00574.2870.013600.000080.006240.005640.018730.023080.2560.012680.013650.016670.038040.1071517.88300.4075670.655683-6.7871970.1584532.6797720.131728
193phon_R01_S50_5198.764396.96174.9040.007400.000040.003700.003900.011090.022960.2410.012650.013210.015880.037940.0722319.02000.4512210.643956-6.7445770.2074542.1386080.123306
194phon_R01_S50_6214.289260.27777.9730.005670.000030.002950.003170.008850.018840.1900.010260.011610.013730.030780.0439821.20900.4628030.664357-5.7240560.1906672.5554770.148569